Unreliable Networks Research Articles

Iterative learning robust security predictive tracking control for small delay batch processes: Deception attacks on unreliable network channel

During the batch process, the need for security control is becoming increasingly urgent with the gradual penetration of network control technology. For small time delay batch processes subject to deception attacks, an iterative learning robust security predictive tracking control approach is developed. Reviewing previous studies for iterative learning control, the issue that the repetitive character of the batch process would mask the characteristics of deception attacks was not considered. Moreover, the gains of iterative learning control law are utilized offline, which makes large deviations for the system state as the operating time increases. This will lead to “excessive-input” conditions for control inputs, which means more consumption of energy and production resources. To address these challenges, we introduce robust security invariant sets to improve the robustness of the system by constraining the system state to be in a safe range. Also, the design of the iterative learning controller incorporates deception attack data for historical batches, which is continuously improved and optimized to withstand deception attacks. Meanwhile, by calculating stability conditions online, the real-time control law gain is obtained, which could make the control inputs adjusted online based on the real-time system’s dynamic characteristics, avoiding excessive inputs and improving the efficiency of energy and resource utilization. Additionally, the stability analysis proves that the system is input to state stability. Ultimately, simulation verifies the effectiveness and feasibility of the developed method.

P018 IMPLEMENTATION CONSIDERATIONS FOR SMS REMINDERS TO IMPROVE ANTIHYPERTENSIVE MEDICATION ADHERENCE IN BANGLADESH AND ETHIOPIA: A QUALITATIVE EVALUATION

Background: Poor medication adherence remains a significant barrier to hypertension control, particularly in resource-limited settings. Objective: To qualitatively explore the feasibility and acceptability of SMS reminders to support antihypertensive medication adherence and identify local nuances of barriers, facilitators, and implementation considerations in resource-limited settings. Methods: Patients with hypertension and providers in primary care clinics in Ethiopia and Bangladesh were interviewed to assess perceptions and experiences with SMS reminders. Interviews were transcribed and translated, an inductive codebook was developed, thematic content analysis was used to identify key implementation considerations. The Consolidated Framework for Implementation Research (CFIR) constructs of innovation, outer, inner settings, individual-level factors were used to synthesize findings. Results: Fifty patients (mean age 53.1±13.1 years, 28(56%) female, 28(56%) from Ethiopia) and 50 providers (mean age 32±7.5 years, 24(48%) female, 27(54%) from Ethiopia) were interviewed. Facilitators included the perceived value of SMS reminders for adherence (patients) and improved patient-provider communication (providers). Patients and providers generally viewed SMS reminders as valuable for supporting adherence (innovation). However, concerns were raised about the timing of message delivery and the potential for missed messages. Unreliable network infrastructure was identified as a potential barrier, particularly in rural areas (outer setting). The absence of electronic medical and pharmacy records in many clinics was a reported challenge for automating reminders; providers highlighted the need for dedicated IT support and staff (inner setting). Focusing on the highest-need patients was suggested as a practical approach to begin implementation. Patient's access to mobile phones, literacy levels, and receptiveness to reminders were key individual-level factors that could influence engagement with SMS reminders. Providers emphasized that reminders may not be effective for all patients (Table). Conclusions: SMS reminders can potentially improve antihypertensive medication adherence in resource-limited settings, but several implementation barriers need to be addressed. Tailoring SMS reminders to patient needs, strengthening infrastructure, and providing adequate IT and staff support are essential for successful implementation.

Evaluating the role of cloud integration in mobile and desktop operating systems

The integration of cloud computing has fundamentally transformed the landscape of mobile and desktop operating systems, enabling new levels of functionality, efficiency, and user experience. This review examines the role of cloud integration in these operating systems and its implications for both users and developers. Cloud integration enhances mobile and desktop operating systems by offering scalable storage solutions, seamless data synchronization, and enhanced computing power. For mobile systems, cloud services provide users with the ability to access their data and applications from any device, promoting a consistent user experience across different platforms. This synchronization feature not only streamlines personal data management but also enables developers to create more dynamic and responsive applications that leverage cloud resources for realtime updates and performance improvements. In desktop environments, cloud integration facilitates the storage of large volumes of data and the execution of resourceintensive applications without burdening local hardware. Cloudbased virtual machines and application delivery platforms offer users the flexibility to run highperformance software on less powerful devices, thus broadening the accessibility and usability of advanced desktop applications. Furthermore, cloud integration supports collaborative tools and shared workspaces, enhancing productivity and fostering realtime collaboration among users. The evaluation of cloud integration also involves assessing its impact on security, data privacy, and system performance. Cloudbased solutions introduce new security considerations, such as the need for robust encryption and secure authentication protocols to protect sensitive data. Additionally, the reliance on internet connectivity for accessing cloud services can pose challenges for users in areas with limited or unreliable network access. In conclusion, cloud integration plays a crucial role in modernizing both mobile and desktop operating systems, driving improvements in data accessibility, application performance, and user experience. As cloud technologies continue to evolve, their influence on operating systems will likely expand, offering new opportunities for innovation and addressing emerging challenges in security and connectivity. This ongoing integration represents a significant shift towards more flexible, efficient, and interconnected computing environments.

A novel central bank digital currency framework design for offline and foreign transactions based on blockchain

Purpose Fiat money production necessitates physical commodities, increasing costs and its flow is challenging to monitor, making it vulnerable to criminal exploitation. Cryptocurrencies offer decentralized solutions, but their decentralization has led to illegal activities. Current cross-border transactions face high costs, resource intensity and lack of instant currency transfers. Offline transactions are essential in unreliable networks. Design/methodology/approach Here, the authors proposed the methodology to perform offline transactions based on card, quick response (QR) code and a foreign transaction framework with universal identification (UID) to perform cross-border transactions using blockchain-dependent central bank digital currencies (CBDCs). Implications for the financial system are also analyzed. Findings The proposed CBDC framework reduces illegal transactions, corruption and the cost of producing fiat money; eases overseas transactions; and eventually increases international tourism, trade and business between countries. It also reduces the processing fees. Offline framework found useful for performing retail-level transactions. Research limitations/implications The research methodology may face limitations due to diplomatic relations, political instability, sanctions and the need for robust offline transaction infrastructure. Practical implications The proposed CBDC framework simplifies debt and insurance management, tax collection, international trade, tourism and global stock market participation. However, implementing CBDCs in low-income countries presents challenges like extensive training, infrastructure and user acceptance issues. Social implications The adoption of CBDCs can enhance financial stability by reducing corruption and illegal transactions through improved traceability and monitoring, thereby curbing activities like terrorism. Originality/value Common framework for foreign transactions is based on the UID, and offline transaction framework is based on the sender’s QR code for multiple user applications.

Evaluating V2X-Based Vehicle Control under Unreliable Network Conditions, Focusing on Safety Risk

Evaluating V2X-Based Vehicle Control under Unreliable Network Conditions, Focusing on Safety Risk

Filter design for cyber‐physical systems against DoS attacks and unreliable networks: A Markovian approach

Filter design for cyber‐physical systems against DoS attacks and unreliable networks: A Markovian approach

Fuzzy control for networked systems with mixed data missing under quantization based event-triggered scheme

This paper presents an innovative event-triggered fuzzy control approach for networked systems operating over unreliable network channels. Traditional complicated modeling and analysis process of network induced issues is replaced by a bran-new logarithmic quantization based event-triggered scheme method. The joint characterization of mixed data missing, arising from the event-triggered mechanism and unreliable network channels, becomes a challenging issue due to disrupted data sequences and variable relations. Consequently, this paper contributes a formulated auxiliary stochastic sequence approach to overcome the difficulty of dynamics modeling. With an formulated event driven controller, the modeling and implementation of the closed-loop systems are constructed and further simplified. Considering the difference and relation between the membership functions of plant and controller, we present a new fuzzy output feedback controller design method to obtain feasible result via uncertainty based method. Finally, we verify the validity of the designed control policy via a simulation example.

Investigating the impact of unstable network connections on the cluster running a consensus algorithm

This study explores the effects of unstable network connections on the operational efficiency of a computing cluster executing a consensus algorithm. Consensus algorithms are critical for maintaining the integrity and consistency of distributed systems, particularly in blockchain technologies and distributed databases. However, the reliability of these systems is often challenged by network instability, which can lead to delays, decreased throughput, and, in severe cases, loss of consensus. Research employs simulation experiments to quantify the impact of network disruptions such as latency variability on cluster running consensus algorithm. By systematically varying network conditions and observing the resultant effects on a cluster running a representative consensus algorithm, we identify critical thresholds of instability that significantly impair system performance. The findings contribute to a deeper understanding of the dynamics between network stability and distributed system, offering valuable insights for developers and engineers tasked with designing and maintaining robust distributed systems in the face of network unreliability.

Codesign of FDI Attacks Detection, Isolation, and Mitigation for Complex Microgrid Systems: An HBF-NN-Based Approach.

The primary purpose of this article is to design an intelligent false data injection (FDI) attacks detection, isolation, and mitigation scheme for a class of complex microgrid systems with electric vehicles (EVs). First, a networked microgrid with an EV model is well established, which takes load disturbance, wind generation fluctuation, and FDI attacks into account so as to truly reflect the operation process of the complex system. Then, an intelligent hyper basis function neural network (HBF-NN) observer is designed to accurately estimate the state of the microgrids, learn, and reconstruct the possible attack signal online. Subsequently, a novel HBF-NN-based H∞ controller is skillfully designed to mitigate the negative impact of FDI attacks online, so as to ensure the normal operation of the complex systems in an unreliable network environment. Finally, a two-stage integrated intelligent detection and maintenance algorithm is summarized and one simulation is presented to provide tangible evidence of the feasibility and superiority of the proposed FDI attacks detection, isolation, and mitigation methodology.

Asynchronous Federated Learning with Grey Wolf Optimization for the Heterogeneity IoT Devices

The Internet of Things (IoT) creates new options for real-time data collection and Machine Learning (ML) model development. Nonetheless, it's feasible that a particular IoT device does not have sufficient computational power to train and implement a complete learning model. However, there are significant communication costs and data security and privacy concerns associated with sending actual data to a centralised server with a lot of computational power. Federated Learning (FL) is a potential way to train ML models using low-powered devices and Edge Servers (ES) since it is a distributed ML architecture. However, the vast majority of the works in existence make the unsustainable assumption of a synchronized parameters update manner with similar IoT nodes and reliable communications networks. To increase training efficiency and accelerate the speed for heterogeneous IoT devices in an unreliable network environment, we designed an Asynchronous Federated Learning strategy with Grey Wolf Optimization (AFL-GWO) in this research. In particular, we develop a Lightweight Node Selection (LNS) technique and propose an AFL-GWO model to efficiently complete learning tasks. To ensure that diverse IoT nodes with varying computational capabilities and network connectivity are represented in the global learning aggregate, the proposed technique makes node selections on an iterative basis. We show through extensive experiments that our suggested AFL-GWO system outperforms the state-of-the-art techniques on identically and independently distributed (IID) and non-IID data distribution in a variety of contexts.

Enhancing secure financial transactions through the synergy of blockchain and artificial intelligence

Blockchain and artificial intelligence are innovative technologies that can securely process and share data across unreliable networks. Due to data leakage from user information, it is critical to keep the data confidential and completely protected because criminals are looking for this information to attack the system or steal information in the banking sector. To address this issue, this paper suggests an Integrated Blockchain and Artificial intelligence (IBAI) Framework for secure financial transactions. A blockchain can store every customer's data in one place, while AI-driven algorithms can speedily examine that data and make an unbiased decision. The rising blockchain technology provides a decentralized architecture that enables the secure sharing of data and resources to the different networks and is promoted for removing centralized control and resolving the problems of AI. When suspicious behaviour happens, alerts can be triggered to avert theft. Furthermore, registration protocols based on AI are utilized to keep this data in comprehensive security. The numerical results show that the suggested IBAI model enhances the suspicious behaviour detection ratio and increases accuracy up to 98% compared to other models.

Communication Efficient Distributed Newton Method over Unreliable Networks

Distributed optimization in resource constrained devices demands both communication efficiency and fast convergence rates. Newton-type methods are getting preferable due to their superior convergence rates compared to the first-order methods. In this paper, we study a new problem in regard to the second-order distributed optimization over unreliable networks. The working devices are power-limited or operate in unfavorable wireless channels, experiencing packet losses during their uplink transmission to the server. Our scenario is very common in real-world and leads to instability of classical distributed optimization methods especially the second-order methods because of their sensitivity to the imprecision of local Hessian matrices. To achieve robustness to high packet loss, communication efficiency and fast convergence rates, we propose a novel distributed second-order method, called RED-New (Packet loss Resilient Distributed Approximate Newton). Each iteration of RED-New comprises two rounds of light-weight and lossy transmissions, in which the server aggregates the local information with a new developed scaling strategy. We prove the linear-quadratic convergence rate of RED-New. Experimental results demonstrate its advantage over first-order and second-order baselines, and its tolerance to packet loss rate ranging from 5% to 40%.

Improving Data Delivery in Unreliable Networks Using Network Coding and Ant-Colony Optimization

Wireless Sensor Networks (WSNs) comprise interconnected wireless nodes that receive and transmit data across applications and platforms. This paper addressed the problem of link failures in WSNs that potentially could lead to the loss of data packets while still in transit. This was achieved through the use of network coding which is known to address capacity bottleneck problems in WSNs. In particular, a technique called Ant Agent-Assisted Network Coding (AAANC) is proposed that employs the ant colony optimization technique in addition to network coding operations. The main aim of AAANC is to facilitate the successful delivery and decoding of coded data packets in the presence of link failures. AAANC employs a packet route selection technique that is inspired by the social behavior of natural ants. For natural ants, a strong pheromone trail along a path indicates a promising route to a food source, and this is analogous to a reliable communication link for routing data packets in this paper. Through simulations, AAANC was compared to diagonal pseudorandom network coding (DNC) and triangular pseudorandom network coding (TNC), and it proved to have a superior performance in terms of packet delivery ratio and number of decoded packets. Significant performance gain can be achieved if AAANC algorithm is made to dynamically adapt the ant colony and network coding parameters in response to traffic changes.

Adaptive fault‐tolerant platooning control for nonlinear homogeneous multiple autonomous vehicles under unreliable communication networks

AbstractThis article presents a fault‐tolerant platooning control algorithm for a class of nonlinear homogeneous multi‐vehicle systems under unreliable network communication environment. The considered types of actuator failures in individual vehicle are loss‐in‐effectiveness, and bias faults. Suppose the communication network among vehicles is here described as a switching graph subject to intermittent communications. Unlike the existing results dealing with vehicle platoon under continuous communications, this article develops a hierarchical fault‐tolerant platooning control approach, under which, the multiple vehicle coordination platooning objective is ensured even in the cases of discontinuous communication faults and actuator failures. First, each following vehicle can estimate the desire reference information via cooperative adaptive virtual estimator even in intermittent communications. Second, the local fault‐tolerant controller with adaptive parameter update mechanism can guarantee the practical position and velocity of following vehicles asymptotically track the desire estimations. Finally, a simulation example of a team of autonomous vehicles is given to verify the effectiveness of the proposed method.

Network Traffic Characteristics and Analysis in Recent Mobile Games

The landscape of mobile gaming has evolved significantly over the years, with profound changes in network reliability and traffic patterns. In the early 2010s, mobile games faced challenges due to unreliable networks and primarily featured asynchronous gameplay. However, in the current era, modern mobile games benefit from robust network connectivity, mirroring PC gaming experiences by relying on persistent connections to game servers. This shift prompted us to conduct an in-depth traffic analysis of two mobile games that represent opposite ends of the genre spectrum: a massively multiplayer game resembling PC MMORPGs with tightly synchronized gameplay, and a single-player puzzle game that incorporates asynchronous social interactions. Surprisingly, both games exhibited remarkably similar traffic footprints; small packets with short inter-packet arrival times, indicating their high expectations for network reliability. This suggests that game developers now prioritize network quality similarly to their PC gaming counterparts. Additionally, our analysis of packet lengths unveiled that recent mobile games predominantly employ short packets dominated by a few key packet types closely tied to player actions, which conforms to observations from PC online games. However, the self-similarity in traffic patterns, a notable feature in PC online games, only partially explains the traffic in mobile games, varying across genres. These findings shed light on the evolving traffic patterns in mobile games and emphasize the need for further research in this dynamic domain.

Dynamic Event-Triggered Scaled Consensus of Multi-Agent Systems in Reliable and Unreliable Networks

Dynamic Event-Triggered Scaled Consensus of Multi-Agent Systems in Reliable and Unreliable Networks

Robust Channels: Handling Unreliable Networks in the Record Layers of QUIC and DTLS 1.3

The common approach in secure communication channel protocols is to rely on ciphertexts arriving in-order and to close the connection upon any rogue ciphertext. Cryptographic security models for channels generally reflect such design. This is reasonable when running atop lower-level transport protocols like TCP ensuring in-order delivery, as for example, is the case with TLS or SSH. However, protocols like QUIC or DTLS which run over a non-reliable transport such as UDP, do not—and in fact cannot—close the connection if packets are lost or arrive in a different order. Those protocols instead have to carefully catch effects arising naturally in unreliable networks, usually by using a sliding-window technique where ciphertexts can be decrypted correctly as long as they are not misplaced too far. In order to be able to capture QUIC and the newest DTLS version 1.3, we introduce a generalized notion of robustness of cryptographic channels. This property can capture unreliable network behavior and guarantees that adversarial tampering cannot hinder ciphertexts that can be decrypted correctly from being accepted. We show that robustness is orthogonal to the common notion of integrity for channels, but together with integrity and chosen-plaintext security it provides a robust analog of chosen-ciphertext security of channels. In contrast to prior work, robustness allows us to study packet encryption in the record layer protocols of QUIC and of DTLS 1.3 and the novel sliding-window techniques both protocols employ. We show that both protocols achieve robust chosen-ciphertext security based on certain properties of their sliding-window techniques and the underlying AEAD schemes. Notably, the robustness needed in handling unreliable network messages requires both record layer protocols to tolerate repeated adversarial forgery attempts. This means we can only establish non-tight security bounds (in terms of AEAD integrity), a security degradation that was missed in earlier protocol drafts. Our bounds led the responsible IETF working groups to introduce concrete forgery limits for both protocols and the IRTF CFRG to consider AEAD usage limits more broadly.

Event-Triggered State Filter Estimation for Nonlinear Systems with Packet Dropout and Correlated Noise

This paper begins by exploring the challenge of event-triggered state estimations in nonlinear systems, grappling with packet dropout and correlated noise. A communication mechanism is introduced that determines whether to transmit measurement values based on whether event-triggered conditions are violated, thereby minimizing redundant communication data. In designing the filter, noise decorrelation is initially conducted, followed by the integration of the event-triggered mechanism and the unreliable network transmission system for state estimator development. Subsequently, by combining the three-degree spherical–radial cubature rule, the numerical implementation steps of the proposed state estimation framework are outlined. The performance estimation analysis highlights that by adjusting the event-triggered threshold appropriately, the estimation performance and transmission rate can be effectively balanced. It is established that when there is a lower bound on the packet dropout rate, the covariance matrix of the state estimation error remains bounded, and the stochastic stability of the state estimation error is also confirmed. Ultimately, the algorithm and conclusions that are proposed in this paper are validated through a simulation example of a target tracking system.

LoRe: A Programming Model for Verifiably Safe Local-first Software

Local-first software manages and processes private data locally while still enabling collaboration between multiple parties connected via partially unreliable networks. Such software typically involves interactions with users and the execution environment (the outside world). The unpredictability of such interactions paired with their decentralized nature make reasoning about the correctness of local-first software a challenging endeavor. Yet, existing solutions to develop local-first software do not provide support for automated safety guarantees and instead expect developers to reason about concurrent interactions in an environment with unreliable network conditions. We propose LoRe , a programming model and compiler that automatically verifies developer-supplied safety properties for local-first applications. LoRe combines the declarative data flow of reactive programming with static analysis and verification techniques to precisely determine concurrent interactions that violate safety invariants and to selectively employ strong consistency through coordination where required. We propose a formalized proof principle and demonstrate how to automate the process in a prototype implementation that outputs verified executable code. Our evaluation shows that LoRe simplifies the development of safe local-first software when compared to state-of-the-art approaches and that verification times are acceptable.

Architectural Design of IoT Agricultural System in Indonesia

Indonesia’s agricultural system is gradually transitioning towards a contemporary agricultural system. The agricultural system in Indonesia is characterized by its distinctiveness, encompassing a wide range of commodities and diverse meteorological conditions. Situations of this nature necessitate an automation approach based on the Internet of Things (IoT). We design three primary components in its architectural design: the user control and monitoring module, the sensing module, and the action module. The user control and monitoring module facilitates instantaneous monitoring and control through mobile devices and websites, while securely storing gathered data on remote cloud servers. The sensing module acquires environmental data, including light intensity, temperature, and soil moisture, and subsequently transmits this data to the control system via LoRa technology. The control system analyses this data and provides commands to modules that carry out operations such as irrigation, lighting, and nutrient spraying. LoRa technology is highly suitable for long-range communications with minimal power usage, making it an excellent choice for rural locations with unreliable networks. This architectural design enables independent monitoring and control of environmental factors to guarantee the most favourable conditions for crop development, consequently enhancing agricultural efficiency, productivity, and sustainability.